Azospirillum brasilense as a bioinoculant to alleviate salinity effects on quinoa seed germination

1 Project Setup

Code

library(GerminaR)
library(scales)
source('https://inkaverse.com/setup.r')

Project directory:  C:/Users/LENOVO/git/azospirillum_brasilense 
CPU cores detected:  32 
CPU cores in use:  25

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.1 (2025-06-13 ucrt)
 os       Windows 11 x64 (build 26100)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  Spanish_Latin America.utf8
 ctype    Spanish_Latin America.utf8
 tz       America/Lima
 date     2025-08-21
 pandoc   3.4 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
 quarto   NA @ C:\\PROGRA~1\\Quarto\\bin\\quarto.exe

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version date (UTC) lib source
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 [1] C:/Users/LENOVO/AppData/Local/R/win-library/4.5
 [2] C:/Program Files/R/R-4.5.1/library
 * ── Packages attached to the search path.

──────────────────────────────────────────────────────────────────────────────

2 Base de datos

https://docs.google.com/spreadsheets/d/1t20oZQ_NR4zC8-gwkmV6UUMZwC24Hvh6WNJVtCXCF30/edit?gid=506947099#gid=506947099

3 Import data

Code

gs <- "https://docs.google.com/spreadsheets/d/1t20oZQ_NR4zC8-gwkmV6UUMZwC24Hvh6WNJVtCXCF30/edit?gid=506947099#gid=506947099" %>% 
  as_sheets_id()

bacterial <- gs %>%
  range_read("bacterial") %>%
  rename_with(~ iconv(.x, from = "UTF-8", to = "ASCII//TRANSLIT")) %>% 
  rename_with(~ str_replace_all(.x, "^[^a-zA-Z0-9]+|[^a-zA-Z0-9]+$", "")) %>% 
  rename_with(~ tolower(gsub("[^[:alnum:]_]", "_", .x))) %>% 
  mutate(across(1:repetition, ~ as.factor(.))) %>% 
  dplyr::select(where(~ mean(!is.na(.)) >= 0.1))
  
bacterial %>% str()
## tibble [168 × 5] (S3: tbl_df/tbl/data.frame)
##  $ strains         : Factor w/ 6 levels "BR-11001","BR-11002",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ nacl            : Factor w/ 7 levels "0","150","300",..: 1 1 1 1 2 2 2 2 3 3 ...
##  $ repetition      : Factor w/ 4 levels "R1","R2","R3",..: 1 2 3 4 1 2 3 4 1 2 ...
##  $ od_600_nm       : num [1:168] 1.29 1.3 1.29 1.29 1.3 1.27 1.31 1.27 1.15 1.12 ...
##  $ bacterial_growth: num [1:168] 1558000000 1570000000 1558000000 1558000000 1570000000 ...

bacterial %>% web_table()

Code


germ <- gs %>%
  range_read("radicula") %>%
  rename_with(~ iconv(.x, from = "UTF-8", to = "ASCII//TRANSLIT")) %>% 
  rename_with(~ str_replace_all(.x, "^[^a-zA-Z0-9]+|[^a-zA-Z0-9]+$", "")) %>% 
  rename_with(~ tolower(gsub("[^[:alnum:]_]", "_", .x))) %>% 
  mutate(across(1:nacl, ~ as.factor(.))) %>% 
  dplyr::select(where(~ mean(!is.na(.)) >= 0.1))
  
germ %>% str()
## tibble [48 × 15] (S3: tbl_df/tbl/data.frame)
##  $ repetition: Factor w/ 4 levels "R1","R2","R3",..: 1 2 3 4 1 2 3 4 1 2 ...
##  $ strains   : Factor w/ 3 levels "BR-11001","BR-11002",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ nacl      : Factor w/ 4 levels "0","150","300",..: 1 1 1 1 2 2 2 2 3 3 ...
##  $ seeds     : num [1:48] 50 50 50 50 50 50 50 50 50 50 ...
##  $ d0        : num [1:48] 0 0 0 0 0 0 0 0 0 0 ...
##  $ d1        : num [1:48] 44 43 42 44 37 39 38 36 6 6 ...
##  $ d2        : num [1:48] 3 0 3 3 2 1 4 3 21 24 ...
##  $ d3        : num [1:48] 0 2 0 0 2 2 2 3 17 16 ...
##  $ d4        : num [1:48] 2 3 3 1 4 3 2 4 2 0 ...
##  $ d5        : num [1:48] 0 2 2 2 5 4 2 2 1 1 ...
##  $ d6        : num [1:48] 0 0 0 0 0 0 0 0 0 0 ...
##  $ d7        : num [1:48] 0 0 0 0 0 1 1 2 0 0 ...
##  $ d8        : num [1:48] 1 0 0 0 0 0 1 0 0 0 ...
##  $ d9        : num [1:48] 0 0 0 0 0 0 0 0 0 0 ...
##  $ d10       : num [1:48] 0 0 0 0 0 0 0 0 1 0 ...

germ %>% web_table()

Code


cot <- gs %>%
  range_read("germination") %>%
  rename_with(~ iconv(.x, from = "UTF-8", to = "ASCII//TRANSLIT")) %>% 
  rename_with(~ str_replace_all(.x, "^[^a-zA-Z0-9]+|[^a-zA-Z0-9]+$", "")) %>% 
  rename_with(~ tolower(gsub("[^[:alnum:]_]", "_", .x))) %>% 
  mutate(across(1:nacl, ~ as.factor(.))) %>% 
  dplyr::select(where(~ mean(!is.na(.)) >= 0.1))
  
cot %>% str()
## tibble [48 × 15] (S3: tbl_df/tbl/data.frame)
##  $ repetition: Factor w/ 4 levels "R1","R2","R3",..: 1 2 3 4 1 2 3 4 1 2 ...
##  $ strains   : Factor w/ 3 levels "BR-11001","BR-11002",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ nacl      : Factor w/ 4 levels "0","150","300",..: 1 1 1 1 2 2 2 2 3 3 ...
##  $ seeds     : num [1:48] 50 50 50 50 50 50 50 50 50 50 ...
##  $ d0        : num [1:48] 0 0 0 0 0 0 0 0 0 0 ...
##  $ d1        : num [1:48] 0 0 0 0 0 0 0 0 0 0 ...
##  $ d2        : num [1:48] 0 0 0 0 0 0 0 0 0 0 ...
##  $ d3        : num [1:48] 11 11 11 9 0 2 5 5 0 0 ...
##  $ d4        : num [1:48] 21 17 16 19 14 13 11 9 0 0 ...
##  $ d5        : num [1:48] 1 3 4 4 7 5 7 8 9 7 ...
##  $ d6        : num [1:48] 4 8 9 5 10 6 4 5 9 6 ...
##  $ d7        : num [1:48] 7 8 8 8 5 3 3 7 9 15 ...
##  $ d8        : num [1:48] 6 2 1 5 6 7 7 4 2 2 ...
##  $ d9        : num [1:48] 0 0 0 0 5 9 7 8 1 2 ...
##  $ d10       : num [1:48] 0 0 0 0 2 4 6 3 3 3 ...

cot %>% web_table()

Code


growth <- gs %>%
  range_read("growth") %>%
  rename_with(~ iconv(.x, from = "UTF-8", to = "ASCII//TRANSLIT")) %>% 
  rename_with(~ str_replace_all(.x, "^[^a-zA-Z0-9]+|[^a-zA-Z0-9]+$", "")) %>% 
  rename_with(~ tolower(gsub("[^[:alnum:]_]", "_", .x))) %>% 
  mutate(across(1:repetition, ~ as.factor(.))) %>% 
  dplyr::select(where(~ mean(!is.na(.)) >= 0.1))
  
growth %>% str()
## tibble [48 × 14] (S3: tbl_df/tbl/data.frame)
##  $ strains      : Factor w/ 3 levels "BR-11001","BR-11002",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ nacl         : Factor w/ 4 levels "0","150","300",..: 1 1 1 1 2 2 2 2 3 3 ...
##  $ repetition   : Factor w/ 4 levels "R1","R2","R3",..: 1 2 3 4 1 2 3 4 1 2 ...
##  $ shoot_length : num [1:48] 4.74 4.68 4.82 4.84 4.75 ...
##  $ root_length  : num [1:48] 3.12 2.97 3.07 3.05 3.04 ...
##  $ fresh_weight : num [1:48] 0.343 0.343 0.343 0.343 0.343 ...
##  $ dry_weight   : num [1:48] 0.0268 0.0264 0.0266 0.0257 0.0271 ...
##  $ ms_porc      : num [1:48] 7.81 7.7 7.75 7.5 7.89 ...
##  $ humidity_cont: num [1:48] 0.316 0.316 0.317 0.317 0.316 ...
##  $ humidity_porc: num [1:48] 92.2 92.3 92.3 92.5 92.1 ...
##  $ sod          : num [1:48] 22.9 24 24.1 24.9 34.9 ...
##  $ cat          : num [1:48] 13.1 13.8 14.4 15.3 20.8 ...
##  $ apx          : num [1:48] 8.5 8.99 9.61 10.34 13.7 ...
##  $ gpx          : num [1:48] 5.9 6.54 7.67 8.48 9.3 ...

growth %>% web_table()

Code


# https://colorbrewer2.org/

4 Statistical Analysis

4.1 To determine the growth capacity of BR-11001 and BR-11002 in saline media, as a proxy for their viability and functional persistence under stress.

Code

dtx <- bacterial 
# trait <- "bacterial_growth"

dtx %>% str()
## tibble [168 × 5] (S3: tbl_df/tbl/data.frame)
##  $ strains         : Factor w/ 6 levels "BR-11001","BR-11002",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ nacl            : Factor w/ 7 levels "0","150","300",..: 1 1 1 1 2 2 2 2 3 3 ...
##  $ repetition      : Factor w/ 4 levels "R1","R2","R3",..: 1 2 3 4 1 2 3 4 1 2 ...
##  $ od_600_nm       : num [1:168] 1.29 1.3 1.29 1.29 1.3 1.27 1.31 1.27 1.15 1.12 ...
##  $ bacterial_growth: num [1:168] 1558000000 1570000000 1558000000 1558000000 1570000000 ...

rs <- xfun::cache_rds({
  
  traits <- names(dtx)[4:length(dtx)]
  
  results <- traits %>% set_names() %>% map( \(trait) {
    
    # Liberar memoria
    gc()
    
    # Definir modelo
    mdf <- paste(trait, "0 + strains*nacl + (1|repetition)", sep = " ~ ") 
    
    # Mostrar qué rasgo se analiza
    cat("\n\n## Análisis para: ", trait, "\n\n")
    
    
    cat("\n\n### Diagnostico de modelo\n\n")
    
    # Remover outliers (asumo que tienes esta función personalizada)
    md <- dtx %>% 
      remove_outliers(as.formula(mdf),
                      plot_diag = TRUE,
                      drop_na = TRUE)
    
    md$diagplot %>% print()
    
    if (nrow(md$outliers) > 0) {
    cat("> Outliers detectados\n")
    md$outliers %>% kable() %>% print()
    } else {
    cat("> No se detectaron outliers\n")
    }
    
    mdc <- md$model$clean
    
    cat("\n\n### ANOVA tipo III\n\n")
    
    Anova(mdc, type = 3, test.statistic = "F") %>%  print()
    
    cat("\n\n### Comparaciones múltiples (Emmeans)\n\n")
    
    # Comparaciones múltiples into the groups
    mc_in <- emmeans(mdc, ~ strains|nacl, type = "response") %>% 
      cld(Letters = letters, reversed = TRUE) %>% 
      mutate(across(".group", trimws)) %>% 
      mutate(across(".group", tolower)) %>% 
      rename(sig_in = ".group")
    
    mc_ou <- emmeans(mdc, ~ nacl|strains, type = "response") %>% 
      cld(Letters = letters, reversed = TRUE) %>% 
      mutate(across(".group", trimws)) %>% 
      mutate(across(".group", toupper)) %>% 
      rename(sig_ou = ".group")
    
    mc <- merge(mc_in, mc_ou) %>% 
      unite(col = "sig", c("sig_in", "sig_ou"), sep = "")
    
    mc %>% kable() %>% print()
    
    cat("\n\n### Gráfico resumen\n\n")
    
    plot <- mc %>% 
      plot_smr(type = "line",
               x = "nacl",
               y = "emmean"
               , group = "strains"
               , ylab = trait
               , sig = "sig"
               , error = "SE") +
      scale_color_manual(values = c("#fbb4ae", "#b3cde3", "#ccebc5"
                                    , "#decbe4", "#fed9a6", "#ffffcc"))
    
    plot %>% print()
    
    list(mc = mc,
         plot = plot
         )
  })
  
  results
  
}, file = "result.rds", rerun = T)
## 
## 
## ## Análisis para:  od_600_nm 
## 
## 
## 
## ### Diagnostico de modelo

## > No se detectaron outliers
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: od_600_nm
##                      F Df Df.res                Pr(>F)    
## strains      21795.114  6 121.64 < 0.00000000000000022 ***
## nacl          1645.651  6 123.00 < 0.00000000000000022 ***
## strains:nacl    18.121 30 123.00 < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl | emmean|       SE|  df|  lower.CL|  upper.CL|sig |
## |:--------|:----|------:|--------:|---:|---------:|---------:|:---|
## |BR-11001 |0    | 1.2925| 0.008168| 126| 1.2763358| 1.3086642|bA  |
## |BR-11001 |150  | 1.2875| 0.008168| 126| 1.2713358| 1.3036642|bA  |
## |BR-11001 |300  | 1.1250| 0.008168| 126| 1.1088358| 1.1411642|bB  |
## |BR-11001 |450  | 0.8825| 0.008168| 126| 0.8663358| 0.8986642|bC  |
## |BR-11001 |600  | 0.7000| 0.008168| 126| 0.6838358| 0.7161642|bD  |
## |BR-11001 |750  | 0.6000| 0.008168| 126| 0.5838358| 0.6161642|bE  |
## |BR-11001 |900  | 0.4825| 0.008168| 126| 0.4663358| 0.4986642|bF  |
## |BR-11002 |0    | 1.4700| 0.008168| 126| 1.4538358| 1.4861642|aA  |
## |BR-11002 |150  | 1.4850| 0.008168| 126| 1.4688358| 1.5011642|aA  |
## |BR-11002 |300  | 1.2050| 0.008168| 126| 1.1888358| 1.2211642|aB  |
## |BR-11002 |450  | 1.0025| 0.008168| 126| 0.9863358| 1.0186642|aC  |
## |BR-11002 |600  | 0.7875| 0.008168| 126| 0.7713358| 0.8036642|aD  |
## |BR-11002 |750  | 0.7025| 0.008168| 126| 0.6863358| 0.7186642|aE  |
## |BR-11002 |900  | 0.5625| 0.008168| 126| 0.5463358| 0.5786642|aF  |
## |BR-11003 |0    | 0.9525| 0.008168| 126| 0.9363358| 0.9686642|eA  |
## |BR-11003 |150  | 0.8400| 0.008168| 126| 0.8238358| 0.8561642|eB  |
## |BR-11003 |300  | 0.7725| 0.008168| 126| 0.7563358| 0.7886642|fC  |
## |BR-11003 |450  | 0.6125| 0.008168| 126| 0.5963358| 0.6286642|eD  |
## |BR-11003 |600  | 0.4200| 0.008168| 126| 0.4038358| 0.4361642|eE  |
## |BR-11003 |750  | 0.2650| 0.008168| 126| 0.2488358| 0.2811642|eF  |
## |BR-11003 |900  | 0.1750| 0.008168| 126| 0.1588358| 0.1911642|eG  |
## |BR-11004 |0    | 1.1825| 0.008168| 126| 1.1663358| 1.1986642|cA  |
## |BR-11004 |150  | 1.1775| 0.008168| 126| 1.1613358| 1.1936642|cA  |
## |BR-11004 |300  | 1.0125| 0.008168| 126| 0.9963358| 1.0286642|cB  |
## |BR-11004 |450  | 0.7925| 0.008168| 126| 0.7763358| 0.8086642|cC  |
## |BR-11004 |600  | 0.5900| 0.008168| 126| 0.5738358| 0.6061642|cD  |
## |BR-11004 |750  | 0.4875| 0.008168| 126| 0.4713358| 0.5036642|cE  |
## |BR-11004 |900  | 0.3675| 0.008168| 126| 0.3513358| 0.3836642|cF  |
## |BR-11005 |0    | 1.1550| 0.008168| 126| 1.1388358| 1.1711642|cdA |
## |BR-11005 |150  | 1.1575| 0.008168| 126| 1.1413358| 1.1736642|cA  |
## |BR-11005 |300  | 0.9775| 0.008168| 126| 0.9613358| 0.9936642|dB  |
## |BR-11005 |450  | 0.8000| 0.008168| 126| 0.7838358| 0.8161642|cC  |
## |BR-11005 |600  | 0.5850| 0.008168| 126| 0.5688358| 0.6011642|cdD |
## |BR-11005 |750  | 0.4475| 0.008168| 126| 0.4313358| 0.4636642|dE  |
## |BR-11005 |900  | 0.2950| 0.008168| 126| 0.2788358| 0.3111642|dF  |
## |BR-11006 |0    | 1.1250| 0.008168| 126| 1.1088358| 1.1411642|dA  |
## |BR-11006 |150  | 1.1200| 0.008168| 126| 1.1038358| 1.1361642|dA  |
## |BR-11006 |300  | 0.9275| 0.008168| 126| 0.9113358| 0.9436642|eB  |
## |BR-11006 |450  | 0.7525| 0.008168| 126| 0.7363358| 0.7686642|dC  |
## |BR-11006 |600  | 0.5550| 0.008168| 126| 0.5388358| 0.5711642|dD  |
## |BR-11006 |750  | 0.4375| 0.008168| 126| 0.4213358| 0.4536642|dE  |
## |BR-11006 |900  | 0.2650| 0.008168| 126| 0.2488358| 0.2811642|dF  |
## 
## 
## ### Gráfico resumen

## 
## 
## ## Análisis para:  bacterial_growth 
## 
## 
## 
## ### Diagnostico de modelo

## > No se detectaron outliers
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: bacterial_growth
##                      F Df Df.res                Pr(>F)    
## strains      22094.579  6 121.64 < 0.00000000000000022 ***
## nacl          1645.651  6 123.00 < 0.00000000000000022 ***
## strains:nacl    18.121 30 123.00 < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl |     emmean|      SE|  df|   lower.CL|   upper.CL|sig |
## |:--------|:----|----------:|-------:|---:|----------:|----------:|:---|
## |BR-11001 |0    | 1561000000| 9801603| 126| 1541602915| 1580397085|bA  |
## |BR-11001 |150  | 1555000000| 9801603| 126| 1535602915| 1574397085|bA  |
## |BR-11001 |300  | 1360000000| 9801603| 126| 1340602915| 1379397085|bB  |
## |BR-11001 |450  | 1069000000| 9801603| 126| 1049602915| 1088397085|bC  |
## |BR-11001 |600  |  850000000| 9801603| 126|  830602915|  869397085|bD  |
## |BR-11001 |750  |  730000000| 9801603| 126|  710602915|  749397085|bE  |
## |BR-11001 |900  |  589000000| 9801603| 126|  569602915|  608397085|bF  |
## |BR-11002 |0    | 1774000000| 9801603| 126| 1754602915| 1793397085|aA  |
## |BR-11002 |150  | 1792000000| 9801603| 126| 1772602915| 1811397085|aA  |
## |BR-11002 |300  | 1456000000| 9801603| 126| 1436602915| 1475397085|aB  |
## |BR-11002 |450  | 1213000000| 9801603| 126| 1193602915| 1232397085|aC  |
## |BR-11002 |600  |  955000000| 9801603| 126|  935602915|  974397085|aD  |
## |BR-11002 |750  |  853000000| 9801603| 126|  833602915|  872397085|aE  |
## |BR-11002 |900  |  685000000| 9801603| 126|  665602915|  704397085|aF  |
## |BR-11003 |0    | 1153000000| 9801603| 126| 1133602915| 1172397085|eA  |
## |BR-11003 |150  | 1018000000| 9801603| 126|  998602915| 1037397085|eB  |
## |BR-11003 |300  |  937000000| 9801603| 126|  917602915|  956397085|fC  |
## |BR-11003 |450  |  745000000| 9801603| 126|  725602915|  764397085|eD  |
## |BR-11003 |600  |  514000000| 9801603| 126|  494602915|  533397085|eE  |
## |BR-11003 |750  |  328000000| 9801603| 126|  308602915|  347397085|eF  |
## |BR-11003 |900  |  220000000| 9801603| 126|  200602915|  239397085|eG  |
## |BR-11004 |0    | 1429000000| 9801603| 126| 1409602915| 1448397085|cA  |
## |BR-11004 |150  | 1423000000| 9801603| 126| 1403602915| 1442397085|cA  |
## |BR-11004 |300  | 1225000000| 9801603| 126| 1205602915| 1244397085|cB  |
## |BR-11004 |450  |  961000000| 9801603| 126|  941602915|  980397085|cC  |
## |BR-11004 |600  |  718000000| 9801603| 126|  698602915|  737397085|cD  |
## |BR-11004 |750  |  595000000| 9801603| 126|  575602915|  614397085|cE  |
## |BR-11004 |900  |  451000000| 9801603| 126|  431602915|  470397085|cF  |
## |BR-11005 |0    | 1396000000| 9801603| 126| 1376602915| 1415397085|cdA |
## |BR-11005 |150  | 1399000000| 9801603| 126| 1379602915| 1418397085|cA  |
## |BR-11005 |300  | 1183000000| 9801603| 126| 1163602915| 1202397085|dB  |
## |BR-11005 |450  |  970000000| 9801603| 126|  950602915|  989397085|cC  |
## |BR-11005 |600  |  712000000| 9801603| 126|  692602915|  731397085|cdD |
## |BR-11005 |750  |  547000000| 9801603| 126|  527602915|  566397085|dE  |
## |BR-11005 |900  |  364000000| 9801603| 126|  344602915|  383397085|dF  |
## |BR-11006 |0    | 1360000000| 9801603| 126| 1340602915| 1379397085|dA  |
## |BR-11006 |150  | 1354000000| 9801603| 126| 1334602915| 1373397085|dA  |
## |BR-11006 |300  | 1123000000| 9801603| 126| 1103602915| 1142397085|eB  |
## |BR-11006 |450  |  913000000| 9801603| 126|  893602915|  932397085|dC  |
## |BR-11006 |600  |  676000000| 9801603| 126|  656602915|  695397085|dD  |
## |BR-11006 |750  |  535000000| 9801603| 126|  515602915|  554397085|dE  |
## |BR-11006 |900  |  328000000| 9801603| 126|  308602915|  347397085|dF  |
## 
## 
## ### Gráfico resumen

Code

plots <- list(rs$od_600_nm$plot +
                scale_y_continuous(limits = c(0, 1.75)
                                  , breaks = seq(0, 200, by = 0.25)) +
                labs(x = ""
                     , y = expression(OD[600]~"(nm)")
                     ) #+
                # theme(legend.position = "none"
                #       , axis.text.x = element_blank()
                #       , strip.text = element_blank()
                #       )
              , rs$bacterial_growth$plot +
                scale_y_log10(
                  limits = c(2e8, 20e8),
                  breaks = 10^seq(8, 20, by = 0.1),
                  labels = trans_format("log10", math_format(10^.x))
                  ) +
                labs(x = ""
                     , y = "Bacterial growth"
                     ) +
                theme(legend.position = "none"
                      # , axis.text.x = element_blank()
                      # , strip.text = element_blank()
                      )
              )

legend <- get_plot_component(
  rs$bacterial_growth$plot +
    guides(color = guide_legend(nrow = 1)) +  # Esto fuerza una sola línea
    theme(
      legend.position = "bottom",
      legend.direction = "horizontal",
      legend.box = "horizontal"
    ),
  "guide-box",
  return_all = TRUE
)

fgrids <- plots %>% 
  cowplot::plot_grid(plotlist = ., ncol = 1, labels = "auto") 

export <-  list(legend[[3]], fgrids) %>% 
  cowplot::plot_grid(plotlist = .
                     , ncol = 1
                     , rel_heights = c(0.1, 1))
  
plot <- plots[[1]] %>% 
  ggsave2(plot = ., "manuscript/Figure-01.jpg", width = 16, height = 15
         , units = "cm")

plot %>% include_graphics()

Code


plots[[1]] %>% 
  ggsave2(plot = ., "manuscript/Figure-01.eps", width = 16, height = 15
         , units = "cm")

4.2 To assess the effect of salinity gradient (0 to 450 mM NaCl) on seed germination and cotyledon emergence as indicators of bacterial efficacy

Code

circular_crop <- function(img) {
  # Obtener dimensiones de la imagen
  info <- image_info(img)
  size <- min(info$width, info$height) # Usar la menor dimensión para hacer la imagen cuadrada
  
  # Recortar la imagen a un tamaño cuadrado centrado
  img_square <- image_crop(img, paste0(size, "x", size, "+0+0"))

  # Crear una máscara circular
  mask <- image_blank(size, size, color = "none") %>% # Fondo transparente
    image_draw()
  grid::grid.circle(r = 0.4, gp = grid::gpar(fill = "black", col = NA)) # Círculo negro sobre fondo transparente
  dev.off()

  # Aplicar la máscara (recortar la imagen en un círculo)
  img_circular <- image_composite(img_square, mask, operator = "CopyOpacity")

  # Crear un fondo negro del mismo tamaño
  background <- image_blank(size, size, color = "black")

  # Poner la imagen circular sobre el fondo negro
  img_final <- image_composite(background, img_circular, operator = "Over")

  return(img_final)
}

plates <- list.files(path = "photos/"
                  , full.names = T
                  , recursive = T
                  ) 

lbls <- plates %>% 
  gsub(".*_(.*)", "\\1", .) %>% 
  gsub("^(\\d+)-days", "\\1 days", .) %>% 
  gsub(".png", "", .) 

lbls %>% dput()
## c("BR-11001", "5 days", "8 days", "10 days", "BR-11002", "5 days", 
## "8 days", "10 days", "Control", "5 days", "8 days", "10 days"
## )

grids <- plates %>% 
  map( ~ image_read(.)) %>% 
  # map( ~ circular_crop(.)) %>%
  map( ~ image_transparent(., "black")) %>%
  map( ~ image_ggplot(.))

rw1 <- grids[9:12] %>% 
  plot_grid(plotlist = ., ncol = 4
            , labels = c("", "5 days", "8 days", "10 days")
            , label_x = 0.15
            )

rw2 <- grids[1:4] %>% 
  plot_grid(plotlist = ., ncol = 4)

rw3 <- grids[5:8] %>% 
  plot_grid(plotlist = ., ncol = 4)


plots <- list(rw1, rw2, rw3) %>% 
  plot_grid(plotlist = ., nrow = 3
            , labels = c("Control", "BR-11001", "BR-11002")
            , hjust = 0
            ) %>% 
  ggsave2(plot = ., "manuscript/Figure-02.jpg"
          , width = 25, height = 22, units = "cm")
  
plot %>% include_graphics()

Code


list(rw1, rw2, rw3) %>% 
  plot_grid(plotlist = ., nrow = 3
            , labels = c("Control", "BR-11001", "BR-11002")
            , hjust = 0
            ) %>% 
  ggsave2(plot = ., "manuscript/Figure-02.eps"
          , width = 25, height = 22, units = "cm")

Code

# germination analysis (ten variables)

gsm <- germ %>% 
  as.data.frame() %>% 
  ger_summary(SeedN = "seeds"
              , evalName = "d"
              , data = .
              ) %>% 
  dplyr::select(1:nacl, grp, mgt, unc, syn)

gsm %>% kable()

repetition	strains	nacl	grp	mgt	unc	syn
R1	Control	0	100	1.320000	0.7044586	0.7755102
R2	Control	0	100	1.420000	0.8021708	0.7412245
R3	Control	0	100	1.400000	0.8841141	0.7085714
R4	Control	0	100	1.280000	0.7044586	0.7755102
R1	Control	150	100	1.760000	1.3166679	0.5583673
R2	Control	150	100	1.720000	1.2261447	0.6130612
R3	Control	150	100	1.700000	1.3754313	0.5812245
R4	Control	150	100	1.820000	1.4913147	0.5257143
R1	Control	300	96	2.541667	1.8508936	0.3209220
R2	Control	300	94	2.276596	1.5216341	0.3802035
R3	Control	300	96	2.562500	1.5333514	0.3785461
R4	Control	300	96	2.458333	1.8861784	0.2969858
R1	Control	450	56	5.714286	2.9686427	0.1111111
R2	Control	450	52	5.538462	2.8552794	0.1138462
R3	Control	450	52	6.115385	2.8073905	0.1200000
R4	Control	450	50	5.600000	2.7932697	0.1233333
R1	BR-11001	0	100	1.140000	0.5411884	0.8130612
R2	BR-11001	0	100	1.180000	0.5221792	0.8457143
R3	BR-11001	0	100	1.140000	0.5411884	0.8130612
R4	BR-11001	0	100	1.140000	0.5221792	0.8457143
R1	BR-11001	150	100	1.720000	1.2510470	0.6114286
R2	BR-11001	150	100	1.800000	1.2112160	0.6408163
R3	BR-11001	150	100	1.700000	1.3212425	0.6106122
R4	BR-11001	150	100	1.580000	1.1312160	0.6440816
R1	BR-11001	300	98	2.020408	1.8611296	0.3027211
R2	BR-11001	300	100	2.340000	1.6586919	0.3232653
R3	BR-11001	300	98	2.081633	1.7188777	0.3154762
R4	BR-11001	300	98	2.061224	1.5385791	0.3418367
R1	BR-11001	450	76	5.236842	2.8813999	0.1379801
R2	BR-11001	450	78	5.128205	2.7550705	0.1457490
R3	BR-11001	450	80	5.425000	2.9402410	0.1243590
R4	BR-11001	450	80	5.075000	3.0495818	0.1179487
R1	BR-11002	0	100	1.100000	0.4689956	0.8163265
R2	BR-11002	0	100	1.080000	0.4021792	0.8497959
R3	BR-11002	0	100	1.120000	0.5293609	0.7844898
R4	BR-11002	0	100	1.140000	0.5842388	0.7542857
R1	BR-11002	150	100	1.440000	0.8421708	0.7404082
R2	BR-11002	150	100	1.360000	0.8972685	0.7387755
R3	BR-11002	150	100	1.440000	0.9555658	0.6775510
R4	BR-11002	150	100	1.380000	0.9792118	0.7061224
R1	BR-11002	300	98	1.755102	1.6618510	0.4005102
R2	BR-11002	300	98	1.530612	1.1669527	0.4761905
R3	BR-11002	300	100	1.760000	1.5892751	0.4465306
R4	BR-11002	300	100	1.920000	1.7961182	0.3240816
R1	BR-11002	450	82	3.780488	2.8971467	0.1500000
R2	BR-11002	450	84	3.166667	2.6581963	0.1718931
R3	BR-11002	450	86	3.813954	2.9186325	0.1317829
R4	BR-11002	450	84	3.809524	2.8385464	0.1428571

Code

dt <- gsm %>% 
  mutate(strains = factor(strains
                         , levels = c("Control", "BR-11001", "BR-11002")
                         , ordered = TRUE))

rs <- xfun::cache_rds({
  
  traits <- names(dt)[4:length(dt)]
  # trait <- "grp" 
  
  results <- traits %>% set_names() %>% map( \(trait) {
    
    # Liberar memoria
    gc()
    
    # Definir modelo
    mdf <- paste(trait, "0 + strains*nacl + (1|repetition)", sep = " ~ ") 
    
    # Mostrar qué rasgo se analiza
    cat("\n\n## Análisis para: ", trait, "\n\n")
    
    
    cat("\n\n### Diagnostico de modelo\n\n")
    
    # Remover outliers (asumo que tienes esta función personalizada)
    md <- dt %>% 
      remove_outliers(as.formula(mdf),
                      plot_diag = TRUE,
                      drop_na = TRUE)
    
    md$diagplot %>% print()
    
    if (nrow(md$outliers) > 0) {
    cat("> Outliers detectados\n")
    md$outliers %>% kable() %>% print()
    } else {
    cat("> No se detectaron outliers\n")
    }
    
    mdc <- md$model$raw
    
    cat("\n\n### ANOVA tipo III\n\n")
    
    Anova(mdc, type = 3, test.statistic = "F") %>%  print()
    
    cat("\n\n### Comparaciones múltiples (Emmeans)\n\n")
    
    # Comparaciones múltiples into the groups
    mc_in <- emmeans(mdc, ~ strains|nacl, type = "response") %>% 
      cld(Letters = letters, reversed = TRUE) %>% 
      mutate(across(".group", trimws)) %>% 
      mutate(across(".group", tolower)) %>% 
      rename(sig_in = ".group")
    
    mc_ou <- emmeans(mdc, ~ nacl|strains, type = "response") %>% 
      cld(Letters = letters, reversed = TRUE) %>% 
      mutate(across(".group", trimws)) %>% 
      mutate(across(".group", toupper)) %>% 
      rename(sig_ou = ".group")
    
    mc <- merge(mc_in, mc_ou) %>% 
      unite(col = "sig", c("sig_in", "sig_ou"), sep = "")
    
    mc %>% kable() %>% print()
    
    cat("\n\n### Gráfico resumen\n\n")
    
    plot <- mc %>% 
      plot_smr(type = "line",
               x = "nacl",
               y = "emmean"
               , group = "strains"
               , ylab = trait
               , sig = "sig"
               , error = "SE") +
      scale_color_manual(values = c("#fbb4ae", "#b3cde3", "#ccebc5"))
    
    plot %>% print()
    
    list(mc = mc,
         plot = plot
         )
  })
  
  results
  
}, file = "result.rds", rerun = T)
## 
## 
## ## Análisis para:  grp 
## 
## 
## 
## ### Diagnostico de modelo

## > Outliers detectados
## 
## 
## |   |index |strains  |nacl |repetition | grp| resi|         res_MAD| rawp.BHStud| adjp| bholm|out_flag |
## |:--|:-----|:--------|:----|:----------|---:|----:|---------------:|-----------:|----:|-----:|:--------|
## |9  |9     |Control  |300  |R1         |  96|  0.5|  11865766925951|           0|    0|     0|OUTLIER  |
## |10 |10    |Control  |300  |R2         |  94| -1.5| -35597300777855|           0|    0|     0|OUTLIER  |
## |11 |11    |Control  |300  |R3         |  96|  0.5|  11865766925951|           0|    0|     0|OUTLIER  |
## |12 |12    |Control  |300  |R4         |  96|  0.5|  11865766925951|           0|    0|     0|OUTLIER  |
## |13 |13    |Control  |450  |R1         |  56|  3.5|  83060368481662|           0|    0|     0|OUTLIER  |
## |14 |14    |Control  |450  |R2         |  52| -0.5| -11865766925952|           0|    0|     0|OUTLIER  |
## |15 |15    |Control  |450  |R3         |  52| -0.5| -11865766925952|           0|    0|     0|OUTLIER  |
## |16 |16    |Control  |450  |R4         |  50| -2.5| -59328834629758|           0|    0|     0|OUTLIER  |
## |25 |25    |BR-11001 |300  |R1         |  98| -0.5| -11865766925952|           0|    0|     0|OUTLIER  |
## |26 |26    |BR-11001 |300  |R2         | 100|  1.5|  35597300777855|           0|    0|     0|OUTLIER  |
## |27 |27    |BR-11001 |300  |R3         |  98| -0.5| -11865766925952|           0|    0|     0|OUTLIER  |
## |28 |28    |BR-11001 |300  |R4         |  98| -0.5| -11865766925952|           0|    0|     0|OUTLIER  |
## |29 |29    |BR-11001 |450  |R1         |  76| -2.5| -59328834629758|           0|    0|     0|OUTLIER  |
## |30 |30    |BR-11001 |450  |R2         |  78| -0.5| -11865766925951|           0|    0|     0|OUTLIER  |
## |31 |31    |BR-11001 |450  |R3         |  80|  1.5|  35597300777855|           0|    0|     0|OUTLIER  |
## |32 |32    |BR-11001 |450  |R4         |  80|  1.5|  35597300777855|           0|    0|     0|OUTLIER  |
## |41 |41    |BR-11002 |300  |R1         |  98| -1.0| -23731533851904|           0|    0|     0|OUTLIER  |
## |42 |42    |BR-11002 |300  |R2         |  98| -1.0| -23731533851904|           0|    0|     0|OUTLIER  |
## |43 |43    |BR-11002 |300  |R3         | 100|  1.0|  23731533851903|           0|    0|     0|OUTLIER  |
## |44 |44    |BR-11002 |300  |R4         | 100|  1.0|  23731533851903|           0|    0|     0|OUTLIER  |
## |45 |45    |BR-11002 |450  |R1         |  82| -2.0| -47463067703807|           0|    0|     0|OUTLIER  |
## |47 |47    |BR-11002 |450  |R3         |  86|  2.0|  47463067703807|           0|    0|     0|OUTLIER  |
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: grp
##                     F Df Df.res                Pr(>F)    
## strains      29824.52  3 33.245 < 0.00000000000000022 ***
## nacl          1719.33  3 33.000 < 0.00000000000000022 ***
## strains:nacl   199.08  6 33.000 < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl | emmean|        SE| df| lower.CL|  upper.CL|sig |
## |:--------|:----|------:|---------:|--:|--------:|---------:|:---|
## |BR-11001 |0    |  100.0| 0.5773503| 36| 98.82908| 101.17092|aA  |
## |BR-11001 |150  |  100.0| 0.5773503| 36| 98.82908| 101.17092|aA  |
## |BR-11001 |300  |   98.5| 0.5773503| 36| 97.32908|  99.67092|aA  |
## |BR-11001 |450  |   78.5| 0.5773503| 36| 77.32908|  79.67092|bB  |
## |BR-11002 |0    |  100.0| 0.5773503| 36| 98.82908| 101.17092|aA  |
## |BR-11002 |150  |  100.0| 0.5773503| 36| 98.82908| 101.17092|aA  |
## |BR-11002 |300  |   99.0| 0.5773503| 36| 97.82908| 100.17092|aA  |
## |BR-11002 |450  |   84.0| 0.5773503| 36| 82.82908|  85.17092|aB  |
## |Control  |0    |  100.0| 0.5773503| 36| 98.82908| 101.17092|aA  |
## |Control  |150  |  100.0| 0.5773503| 36| 98.82908| 101.17092|aA  |
## |Control  |300  |   95.5| 0.5773503| 36| 94.32908|  96.67092|bB  |
## |Control  |450  |   52.5| 0.5773503| 36| 51.32908|  53.67092|cC  |
## 
## 
## ### Gráfico resumen

## 
## 
## ## Análisis para:  mgt 
## 
## 
## 
## ### Diagnostico de modelo

## > Outliers detectados
## 
## 
## |   |index |strains  |nacl |repetition |      mgt|       resi|   res_MAD| rawp.BHStud|           adjp|     bholm|out_flag |
## |:--|:-----|:--------|:----|:----------|--------:|----------:|---------:|-----------:|--------------:|---------:|:--------|
## |15 |15    |Control  |450  |R3         | 6.115385|  0.3376700|  3.692582|   0.0002220| 0.000221988925| 0.0104335|OUTLIER  |
## |46 |46    |BR-11002 |450  |R2         | 3.166667| -0.4404369| -4.816386|   0.0000015| 0.000001461813| 0.0000702|OUTLIER  |
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: mgt
##                     F Df Df.res                Pr(>F)    
## strains       219.659  3 28.942 < 0.00000000000000022 ***
## nacl         1555.737  3 33.000 < 0.00000000000000022 ***
## strains:nacl   39.203  6 33.000    0.0000000000001223 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl |   emmean|       SE|       df| lower.CL| upper.CL|sig |
## |:--------|:----|--------:|--------:|--------:|--------:|--------:|:---|
## |BR-11001 |0    | 1.150000| 0.075536| 33.83642| 0.996465| 1.303535|aD  |
## |BR-11001 |150  | 1.700000| 0.075536| 33.83642| 1.546465| 1.853535|aC  |
## |BR-11001 |300  | 2.125816| 0.075536| 33.83642| 1.972281| 2.279351|bB  |
## |BR-11001 |450  | 5.216262| 0.075536| 33.83642| 5.062727| 5.369797|bA  |
## |BR-11002 |0    | 1.110000| 0.075536| 33.83642| 0.956465| 1.263535|aD  |
## |BR-11002 |150  | 1.405000| 0.075536| 33.83642| 1.251465| 1.558535|bC  |
## |BR-11002 |300  | 1.741429| 0.075536| 33.83642| 1.587894| 1.894964|cB  |
## |BR-11002 |450  | 3.642658| 0.075536| 33.83642| 3.489123| 3.796193|cA  |
## |Control  |0    | 1.355000| 0.075536| 33.83642| 1.201465| 1.508535|aD  |
## |Control  |150  | 1.750000| 0.075536| 33.83642| 1.596465| 1.903535|aC  |
## |Control  |300  | 2.459774| 0.075536| 33.83642| 2.306239| 2.613309|aB  |
## |Control  |450  | 5.742033| 0.075536| 33.83642| 5.588498| 5.895568|aA  |
## 
## 
## ### Gráfico resumen

## 
## 
## ## Análisis para:  unc 
## 
## 
## 
## ### Diagnostico de modelo

## > No se detectaron outliers
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: unc
##                     F Df Df.res                Pr(>F)    
## strains       65.2786  3 23.393      0.00000000001624 ***
## nacl         806.2880  3 33.000 < 0.00000000000000022 ***
## strains:nacl   2.9991  6 33.000               0.01887 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl |    emmean|        SE|       df|  lower.CL|  upper.CL|sig |
## |:--------|:----|---------:|---------:|--------:|---------:|---------:|:---|
## |BR-11001 |0    | 0.5316838| 0.0649248| 26.43367| 0.3983355| 0.6650321|bD  |
## |BR-11001 |150  | 1.2286803| 0.0649248| 26.43367| 1.0953321| 1.3620286|aC  |
## |BR-11001 |300  | 1.6943196| 0.0649248| 26.43367| 1.5609713| 1.8276679|aB  |
## |BR-11001 |450  | 2.9065733| 0.0649248| 26.43367| 2.7732250| 3.0399216|aA  |
## |BR-11002 |0    | 0.4961936| 0.0649248| 26.43367| 0.3628453| 0.6295419|bD  |
## |BR-11002 |150  | 0.9185542| 0.0649248| 26.43367| 0.7852059| 1.0519025|bC  |
## |BR-11002 |300  | 1.5535492| 0.0649248| 26.43367| 1.4202010| 1.6868975|aB  |
## |BR-11002 |450  | 2.8281305| 0.0649248| 26.43367| 2.6947822| 2.9614788|aA  |
## |Control  |0    | 0.7738005| 0.0649248| 26.43367| 0.6404522| 0.9071488|aD  |
## |Control  |150  | 1.3523896| 0.0649248| 26.43367| 1.2190414| 1.4857379|aC  |
## |Control  |300  | 1.6980144| 0.0649248| 26.43367| 1.5646661| 1.8313627|aB  |
## |Control  |450  | 2.8561455| 0.0649248| 26.43367| 2.7227973| 2.9894938|aA  |
## 
## 
## ### Gráfico resumen

## 
## 
## ## Análisis para:  syn 
## 
## 
## 
## ### Diagnostico de modelo

## > No se detectaron outliers
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: syn
##                      F Df Df.res                Pr(>F)    
## strains      1743.0005  3 23.735 < 0.00000000000000022 ***
## nacl         1196.5978  3 33.000 < 0.00000000000000022 ***
## strains:nacl    6.1427  6 33.000             0.0002109 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl |    emmean|        SE|       df|  lower.CL|  upper.CL|sig |
## |:--------|:----|---------:|---------:|--------:|---------:|---------:|:---|
## |BR-11001 |0    | 0.8293878| 0.0161694| 27.01915| 0.7962120| 0.8625635|aA  |
## |BR-11001 |150  | 0.6267347| 0.0161694| 27.01915| 0.5935589| 0.6599105|bB  |
## |BR-11001 |300  | 0.3208248| 0.0161694| 27.01915| 0.2876491| 0.3540006|bC  |
## |BR-11001 |450  | 0.1315092| 0.0161694| 27.01915| 0.0983334| 0.1646850|aD  |
## |BR-11002 |0    | 0.8012245| 0.0161694| 27.01915| 0.7680487| 0.8344003|aA  |
## |BR-11002 |150  | 0.7157143| 0.0161694| 27.01915| 0.6825385| 0.7488901|aB  |
## |BR-11002 |300  | 0.4118282| 0.0161694| 27.01915| 0.3786525| 0.4450040|aC  |
## |BR-11002 |450  | 0.1491333| 0.0161694| 27.01915| 0.1159575| 0.1823091|aD  |
## |Control  |0    | 0.7502041| 0.0161694| 27.01915| 0.7170283| 0.7833799|bA  |
## |Control  |150  | 0.5695918| 0.0161694| 27.01915| 0.5364161| 0.6027676|cB  |
## |Control  |300  | 0.3441644| 0.0161694| 27.01915| 0.3109886| 0.3773401|bC  |
## |Control  |450  | 0.1170726| 0.0161694| 27.01915| 0.0838969| 0.1502484|aD  |
## 
## 
## ### Gráfico resumen

Code

plots <- list(rs$grp$plot +
                scale_y_continuous(limits = c(50, 110)
                                  , breaks = seq(0, 200, by = 10)) +
                labs(x = ""
                     , y = "Germination percentage (%)"
                     ) +
                theme(legend.position = "none"
                      , axis.text.x = element_blank()
                      , strip.text = element_blank()
                      )
              , rs$mgt$plot +
                scale_y_continuous(limits = c(1, 6)
                                  , breaks = seq(0, 200, by = 1)) +
                labs(x = ""
                     , y = "Mean germination time (days)"
                     ) +
                theme(legend.position = "none"
                      , axis.text.x = element_blank()
                      , strip.text = element_blank()
                      )
              , rs$unc$plot +
                scale_y_continuous(limits = c(0.25, 3.25)
                                  , breaks = seq(0, 100, by = 0.5)) +
                labs(x = "NaCl (mM)"
                     , y = "Germination uncertainty"
                     ) +
                theme(legend.position = "none"
                      )
              , rs$syn$plot +
                scale_y_continuous(limits = c(0, 1)
                                  , breaks = seq(0, 100, by = 0.25)) +
                labs(x = "NaCl (mM)"
                     , y = "Germination synchrony"
                     ) +
                theme(legend.position = "none"
                      , axis.text.y = element_text(angle = 90)
                      )
              )

# extraer leyenda
legend <- get_plot_component(rs$grp$plot +
                               theme(legend.position = "top"
                                     , legend.direction = "horizontal")
                             , "guide-box", return_all = T)

fgrids <- plots %>% 
  cowplot::plot_grid(plotlist = ., ncol = 2, labels = "auto") 

export <-  list(legend[[4]], fgrids) %>% 
  cowplot::plot_grid(plotlist = .
                     , ncol = 1
                     , rel_heights = c(0.1, 1))
  
plot <- export %>% 
  ggsave(plot = ., "manuscript/Figure-03.jpg", width = 20, height = 18
         , units = "cm")

plot %>% include_graphics()

Code


export %>% 
  ggsave(plot = ., "manuscript/Figure-03.eps", width = 20, height = 18
         , units = "cm")

4.3 To quantify seedling growth by measuring shoot and root length and dry biomass in inoculated versus non-inoculated treatments under saline and non-saline conditions

Code

dtx <- growth %>% 
  mutate(strains = factor(strains
                         , levels = c("Control", "BR-11001", "BR-11002")
                         , ordered = TRUE)) %>% 
  merge(. , gsm) %>% 
  mutate(svi = (shoot_length + root_length)*grp)


# trait <- "shoot_length" 

rs <- xfun::cache_rds({
  
  traits <- names(dtx)[4:length(dtx)]
  
  results <- traits %>% set_names() %>% map( \(trait) {
    
    # Liberar memoria
    gc()
    
    # Definir modelo
    mdf <- paste(trait, "0 + strains*nacl + (1|repetition)", sep = " ~ ") 
    
    # Mostrar qué rasgo se analiza
    cat("\n\n## Análisis para: ", trait, "\n\n")
    
    
    cat("\n\n### Diagnostico de modelo\n\n")
    
    # Remover outliers (asumo que tienes esta función personalizada)
    md <- dtx %>% 
      remove_outliers(as.formula(mdf),
                      plot_diag = TRUE,
                      drop_na = TRUE)
    
    md$diagplot %>% print()
    
    if (nrow(md$outliers) > 0) {
    cat("> Outliers detectados\n")
    md$outliers %>% kable() %>% print()
    } else {
    cat("> No se detectaron outliers\n")
    }
    
    mdc <- md$model$clean
    
    cat("\n\n### ANOVA tipo III\n\n")
    
    Anova(mdc, type = 3, test.statistic = "F") %>%  print()
    
    cat("\n\n### Comparaciones múltiples (Emmeans)\n\n")
    
    # Comparaciones múltiples into the groups
    mc_in <- emmeans(mdc, ~ strains|nacl, type = "response") %>% 
      cld(Letters = letters, reversed = TRUE) %>% 
      mutate(across(".group", trimws)) %>% 
      mutate(across(".group", tolower)) %>% 
      rename(sig_in = ".group")
    
    mc_ou <- emmeans(mdc, ~ nacl|strains, type = "response") %>% 
      cld(Letters = letters, reversed = TRUE) %>% 
      mutate(across(".group", trimws)) %>% 
      mutate(across(".group", toupper)) %>% 
      rename(sig_ou = ".group")
    
    mc <- merge(mc_in, mc_ou) %>% 
      unite(col = "sig", c("sig_in", "sig_ou"), sep = "")
    
    mc %>% kable() %>% print()
    
    cat("\n\n### Gráfico resumen\n\n")
    
    plot <- mc %>% 
      plot_smr(type = "line",
               x = "nacl",
               y = "emmean"
               , group = "strains"
               , ylab = trait
               , sig = "sig"
               , error = "SE") +
      scale_color_manual(values = c("#fbb4ae", "#b3cde3", "#ccebc5"))
    
    plot %>% print()
    
    list(mc = mc,
         plot = plot
         )
  })
  
  results
  
}, file = "result.rds", rerun = T)
## 
## 
## ## Análisis para:  shoot_length 
## 
## 
## 
## ### Diagnostico de modelo

## > No se detectaron outliers
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: shoot_length
##                     F Df Df.res                Pr(>F)    
## strains      51368.34  3 33.245 < 0.00000000000000022 ***
## nacl          6868.36  3 33.000 < 0.00000000000000022 ***
## strains:nacl    86.68  6 33.000 < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl |   emmean|        SE| df| lower.CL| upper.CL|sig |
## |:--------|:----|--------:|---------:|--:|--------:|--------:|:---|
## |BR-11001 |0    | 5.809725| 0.0244621| 36| 5.760114| 5.859337|bA  |
## |BR-11001 |150  | 5.797650| 0.0244621| 36| 5.748038| 5.847262|bA  |
## |BR-11001 |300  | 4.558875| 0.0244621| 36| 4.509264| 4.608486|bB  |
## |BR-11001 |450  | 3.062825| 0.0244621| 36| 3.013213| 3.112436|bC  |
## |BR-11002 |0    | 6.020650| 0.0244621| 36| 5.971038| 6.070261|aA  |
## |BR-11002 |150  | 6.033325| 0.0244621| 36| 5.983714| 6.082936|aA  |
## |BR-11002 |300  | 5.262625| 0.0244621| 36| 5.213013| 5.312237|aB  |
## |BR-11002 |450  | 3.959200| 0.0244621| 36| 3.909588| 4.008812|aC  |
## |Control  |0    | 4.770450| 0.0244621| 36| 4.720839| 4.820062|cA  |
## |Control  |150  | 4.727025| 0.0244621| 36| 4.677414| 4.776637|cA  |
## |Control  |300  | 4.136000| 0.0244621| 36| 4.086388| 4.185612|cB  |
## |Control  |450  | 2.110125| 0.0244621| 36| 2.060513| 2.159737|cC  |
## 
## 
## ### Gráfico resumen

## 
## 
## ## Análisis para:  root_length 
## 
## 
## 
## ### Diagnostico de modelo

## > No se detectaron outliers
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: root_length
##                      F Df Df.res                Pr(>F)    
## strains      16443.532  3 31.751 < 0.00000000000000022 ***
## nacl          2541.001  3 33.000 < 0.00000000000000022 ***
## strains:nacl    86.177  6 33.000 < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl |   emmean|        SE|       df|  lower.CL|  upper.CL|sig |
## |:--------|:----|--------:|---------:|--------:|---------:|---------:|:---|
## |BR-11001 |0    | 3.316350| 0.0260871| 35.67545| 3.2634262| 3.3692738|bA  |
## |BR-11001 |150  | 3.299150| 0.0260871| 35.67545| 3.2462262| 3.3520738|bA  |
## |BR-11001 |300  | 2.493275| 0.0260871| 35.67545| 2.4403512| 2.5461988|bB  |
## |BR-11001 |450  | 2.097775| 0.0260871| 35.67545| 2.0448512| 2.1506988|bC  |
## |BR-11002 |0    | 3.934625| 0.0260871| 35.67545| 3.8817012| 3.9875488|aA  |
## |BR-11002 |150  | 3.983000| 0.0260871| 35.67545| 3.9300762| 4.0359238|aA  |
## |BR-11002 |300  | 3.457075| 0.0260871| 35.67545| 3.4041512| 3.5099988|aB  |
## |BR-11002 |450  | 2.543650| 0.0260871| 35.67545| 2.4907262| 2.5965738|aC  |
## |Control  |0    | 3.051550| 0.0260871| 35.67545| 2.9986262| 3.1044738|cA  |
## |Control  |150  | 2.999075| 0.0260871| 35.67545| 2.9461512| 3.0519988|cA  |
## |Control  |300  | 2.153075| 0.0260871| 35.67545| 2.1001512| 2.2059988|cB  |
## |Control  |450  | 0.933900| 0.0260871| 35.67545| 0.8809762| 0.9868238|cC  |
## 
## 
## ### Gráfico resumen

## 
## 
## ## Análisis para:  fresh_weight 
## 
## 
## 
## ### Diagnostico de modelo

## > No se detectaron outliers
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: fresh_weight
##                    F Df Df.res                Pr(>F)    
## strains      2441756  3 33.245 < 0.00000000000000022 ***
## nacl          337881  3 33.000 < 0.00000000000000022 ***
## strains:nacl   23722  6 33.000 < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl |   emmean|        SE| df|  lower.CL|  upper.CL|sig |
## |:--------|:----|--------:|---------:|--:|---------:|---------:|:---|
## |BR-11001 |0    | 0.413850| 0.0002851| 36| 0.4132718| 0.4144282|bA  |
## |BR-11001 |150  | 0.414225| 0.0002851| 36| 0.4136468| 0.4148032|bA  |
## |BR-11001 |300  | 0.299875| 0.0002851| 36| 0.2992968| 0.3004532|bB  |
## |BR-11001 |450  | 0.257300| 0.0002851| 36| 0.2567218| 0.2578782|bC  |
## |BR-11002 |0    | 0.556675| 0.0002851| 36| 0.5560968| 0.5572532|aA  |
## |BR-11002 |150  | 0.556750| 0.0002851| 36| 0.5561718| 0.5573282|aA  |
## |BR-11002 |300  | 0.314775| 0.0002851| 36| 0.3141968| 0.3153532|aB  |
## |BR-11002 |450  | 0.300075| 0.0002851| 36| 0.2994968| 0.3006532|aC  |
## |Control  |0    | 0.343000| 0.0002851| 36| 0.3424218| 0.3435782|cA  |
## |Control  |150  | 0.342850| 0.0002851| 36| 0.3422718| 0.3434282|cA  |
## |Control  |300  | 0.257125| 0.0002851| 36| 0.2565468| 0.2577032|cB  |
## |Control  |450  | 0.214125| 0.0002851| 36| 0.2135468| 0.2147032|cC  |
## 
## 
## ### Gráfico resumen

## 
## 
## ## Análisis para:  dry_weight 
## 
## 
## 
## ### Diagnostico de modelo

## > No se detectaron outliers
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: dry_weight
##                     F Df Df.res                Pr(>F)    
## strains      14802.65  3 33.245 < 0.00000000000000022 ***
## nacl          1129.13  3 33.000 < 0.00000000000000022 ***
## strains:nacl   173.93  6 33.000 < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl |   emmean|        SE| df|  lower.CL|  upper.CL|sig |
## |:--------|:----|--------:|---------:|--:|---------:|---------:|:---|
## |BR-11001 |0    | 0.032300| 0.0003048| 36| 0.0316818| 0.0329182|bA  |
## |BR-11001 |150  | 0.032475| 0.0003048| 36| 0.0318568| 0.0330932|bA  |
## |BR-11001 |300  | 0.027275| 0.0003048| 36| 0.0266568| 0.0278932|bB  |
## |BR-11001 |450  | 0.025775| 0.0003048| 36| 0.0251568| 0.0263932|bC  |
## |BR-11002 |0    | 0.049100| 0.0003048| 36| 0.0484818| 0.0497182|aA  |
## |BR-11002 |150  | 0.049150| 0.0003048| 36| 0.0485318| 0.0497682|aA  |
## |BR-11002 |300  | 0.030475| 0.0003048| 36| 0.0298568| 0.0310932|aB  |
## |BR-11002 |450  | 0.031550| 0.0003048| 36| 0.0309318| 0.0321682|aB  |
## |Control  |0    | 0.026375| 0.0003048| 36| 0.0257568| 0.0269932|cA  |
## |Control  |150  | 0.026350| 0.0003048| 36| 0.0257318| 0.0269682|cA  |
## |Control  |300  | 0.020850| 0.0003048| 36| 0.0202318| 0.0214682|cB  |
## |Control  |450  | 0.018500| 0.0003048| 36| 0.0178818| 0.0191182|cC  |
## 
## 
## ### Gráfico resumen

## 
## 
## ## Análisis para:  ms_porc 
## 
## 
## 
## ### Diagnostico de modelo

## > No se detectaron outliers
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: ms_porc
##                     F Df Df.res                Pr(>F)    
## strains      7712.487  3 32.162 < 0.00000000000000022 ***
## nacl          226.923  3 33.000 < 0.00000000000000022 ***
## strains:nacl   12.429  6 33.000          0.0000002787 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl |    emmean|        SE|       df|  lower.CL|  upper.CL|sig |
## |:--------|:----|---------:|---------:|--------:|---------:|---------:|:---|
## |BR-11001 |0    |  7.804621| 0.0901902| 35.81973|  7.621675|  7.987568|bC  |
## |BR-11001 |150  |  7.839800| 0.0901902| 35.81973|  7.656854|  8.022746|bC  |
## |BR-11001 |300  |  9.094932| 0.0901902| 35.81973|  8.911986|  9.277878|bB  |
## |BR-11001 |450  | 10.017337| 0.0901902| 35.81973|  9.834391| 10.200283|bA  |
## |BR-11002 |0    |  8.820208| 0.0901902| 35.81973|  8.637262|  9.003155|aC  |
## |BR-11002 |150  |  8.827945| 0.0901902| 35.81973|  8.644999|  9.010891|aC  |
## |BR-11002 |300  |  9.681266| 0.0901902| 35.81973|  9.498320|  9.864212|aB  |
## |BR-11002 |450  | 10.513656| 0.0901902| 35.81973| 10.330710| 10.696602|aA  |
## |Control  |0    |  7.689417| 0.0901902| 35.81973|  7.506471|  7.872364|bC  |
## |Control  |150  |  7.685450| 0.0901902| 35.81973|  7.502504|  7.868397|bC  |
## |Control  |300  |  8.108541| 0.0901902| 35.81973|  7.925594|  8.291487|cB  |
## |Control  |450  |  8.639402| 0.0901902| 35.81973|  8.456456|  8.822348|cA  |
## 
## 
## ### Gráfico resumen

## 
## 
## ## Análisis para:  humidity_cont 
## 
## 
## 
## ### Diagnostico de modelo

## > No se detectaron outliers
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: humidity_cont
##                     F Df Df.res                Pr(>F)    
## strains      11634347  3 33.245 < 0.00000000000000022 ***
## nacl          1686160  3 33.000 < 0.00000000000000022 ***
## strains:nacl   111712  6 33.000 < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl |   emmean|        SE| df|  lower.CL|  upper.CL|sig |
## |:--------|:----|--------:|---------:|--:|---------:|---------:|:---|
## |BR-11001 |0    | 0.381550| 0.0001198| 36| 0.3813071| 0.3817929|bA  |
## |BR-11001 |150  | 0.381750| 0.0001198| 36| 0.3815071| 0.3819929|bA  |
## |BR-11001 |300  | 0.272600| 0.0001198| 36| 0.2723571| 0.2728429|bB  |
## |BR-11001 |450  | 0.231525| 0.0001198| 36| 0.2312821| 0.2317679|bC  |
## |BR-11002 |0    | 0.507575| 0.0001198| 36| 0.5073321| 0.5078179|aA  |
## |BR-11002 |150  | 0.507600| 0.0001198| 36| 0.5073571| 0.5078429|aA  |
## |BR-11002 |300  | 0.284300| 0.0001198| 36| 0.2840571| 0.2845429|aB  |
## |BR-11002 |450  | 0.268525| 0.0001198| 36| 0.2682821| 0.2687679|aC  |
## |Control  |0    | 0.316625| 0.0001198| 36| 0.3163821| 0.3168679|cA  |
## |Control  |150  | 0.316500| 0.0001198| 36| 0.3162571| 0.3167429|cA  |
## |Control  |300  | 0.236275| 0.0001198| 36| 0.2360321| 0.2365179|cB  |
## |Control  |450  | 0.195625| 0.0001198| 36| 0.1953821| 0.1958679|cC  |
## 
## 
## ### Gráfico resumen

## 
## 
## ## Análisis para:  humidity_porc 
## 
## 
## 
## ### Diagnostico de modelo

## > No se detectaron outliers
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: humidity_porc
##                       F Df Df.res                Pr(>F)    
## strains      987417.131  3 32.162 < 0.00000000000000022 ***
## nacl            226.923  3 33.000 < 0.00000000000000022 ***
## strains:nacl     12.429  6 33.000          0.0000002787 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl |   emmean|        SE|       df| lower.CL| upper.CL|sig |
## |:--------|:----|--------:|---------:|--------:|--------:|--------:|:---|
## |BR-11001 |0    | 92.19538| 0.0901902| 35.81973| 92.01243| 92.37832|aA  |
## |BR-11001 |150  | 92.16020| 0.0901902| 35.81973| 91.97725| 92.34315|aA  |
## |BR-11001 |300  | 90.90507| 0.0901902| 35.81973| 90.72212| 91.08801|bB  |
## |BR-11001 |450  | 89.98266| 0.0901902| 35.81973| 89.79972| 90.16561|bC  |
## |BR-11002 |0    | 91.17979| 0.0901902| 35.81973| 90.99685| 91.36274|bA  |
## |BR-11002 |150  | 91.17205| 0.0901902| 35.81973| 90.98911| 91.35500|bA  |
## |BR-11002 |300  | 90.31873| 0.0901902| 35.81973| 90.13579| 90.50168|cB  |
## |BR-11002 |450  | 89.48634| 0.0901902| 35.81973| 89.30340| 89.66929|cC  |
## |Control  |0    | 92.31058| 0.0901902| 35.81973| 92.12764| 92.49353|aA  |
## |Control  |150  | 92.31455| 0.0901902| 35.81973| 92.13160| 92.49750|aA  |
## |Control  |300  | 91.89146| 0.0901902| 35.81973| 91.70851| 92.07441|aB  |
## |Control  |450  | 91.36060| 0.0901902| 35.81973| 91.17765| 91.54354|aC  |
## 
## 
## ### Gráfico resumen

## 
## 
## ## Análisis para:  sod 
## 
## 
## 
## ### Diagnostico de modelo

## > No se detectaron outliers
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: sod
##                     F Df Df.res                Pr(>F)    
## strains        869.82  3 11.003    0.0000000000002362 ***
## nacl         57039.15  3 33.000 < 0.00000000000000022 ***
## strains:nacl   310.48  6 33.000 < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl |   emmean|        SE|       df| lower.CL| upper.CL|sig |
## |:--------|:----|--------:|---------:|--------:|--------:|--------:|:---|
## |BR-11001 |0    | 26.78000| 0.5248219| 3.291193| 25.19035| 28.36965|aD  |
## |BR-11001 |150  | 39.41000| 0.5248219| 3.291193| 37.82035| 40.99965|bC  |
## |BR-11001 |300  | 53.43625| 0.5248219| 3.291193| 51.84660| 55.02590|bB  |
## |BR-11001 |450  | 63.35875| 0.5248219| 3.291193| 61.76910| 64.94840|bA  |
## |BR-11002 |0    | 26.67125| 0.5248219| 3.291193| 25.08160| 28.26090|aD  |
## |BR-11002 |150  | 42.10250| 0.5248219| 3.291193| 40.51285| 43.69215|aC  |
## |BR-11002 |300  | 57.90625| 0.5248219| 3.291193| 56.31660| 59.49590|aB  |
## |BR-11002 |450  | 68.11625| 0.5248219| 3.291193| 66.52660| 69.70590|aA  |
## |Control  |0    | 23.96375| 0.5248219| 3.291193| 22.37410| 25.55340|bD  |
## |Control  |150  | 36.23125| 0.5248219| 3.291193| 34.64160| 37.82090|cC  |
## |Control  |300  | 48.24000| 0.5248219| 3.291193| 46.65035| 49.82965|cB  |
## |Control  |450  | 56.25375| 0.5248219| 3.291193| 54.66410| 57.84340|cA  |
## 
## 
## ### Gráfico resumen

## 
## 
## ## Análisis para:  cat 
## 
## 
## 
## ### Diagnostico de modelo

## > No se detectaron outliers
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: cat
##                     F Df Df.res                Pr(>F)    
## strains        392.73  3 11.136      0.00000000001432 ***
## nacl         26515.18  3 33.000 < 0.00000000000000022 ***
## strains:nacl   231.98  6 33.000 < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl |   emmean|        SE|       df| lower.CL| upper.CL|sig |
## |:--------|:----|--------:|---------:|--------:|--------:|--------:|:---|
## |BR-11001 |0    | 14.93625| 0.4490014| 3.456453| 13.60837| 16.26413|bD  |
## |BR-11001 |150  | 25.46625| 0.4490014| 3.456453| 24.13837| 26.79413|bC  |
## |BR-11001 |300  | 35.49500| 0.4490014| 3.456453| 34.16712| 36.82288|bB  |
## |BR-11001 |450  | 41.70000| 0.4490014| 3.456453| 40.37212| 43.02788|bA  |
## |BR-11002 |0    | 16.01250| 0.4490014| 3.456453| 14.68462| 17.34038|aD  |
## |BR-11002 |150  | 26.80125| 0.4490014| 3.456453| 25.47337| 28.12913|aC  |
## |BR-11002 |300  | 39.09125| 0.4490014| 3.456453| 37.76337| 40.41913|aB  |
## |BR-11002 |450  | 46.05875| 0.4490014| 3.456453| 44.73087| 47.38663|aA  |
## |Control  |0    | 14.15125| 0.4490014| 3.456453| 12.82337| 15.47913|cD  |
## |Control  |150  | 21.94750| 0.4490014| 3.456453| 20.61962| 23.27538|cC  |
## |Control  |300  | 31.12375| 0.4490014| 3.456453| 29.79587| 32.45163|cB  |
## |Control  |450  | 35.72500| 0.4490014| 3.456453| 34.39712| 37.05288|cA  |
## 
## 
## ### Gráfico resumen

## 
## 
## ## Análisis para:  apx 
## 
## 
## 
## ### Diagnostico de modelo

## > No se detectaron outliers
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: apx
##                     F Df Df.res                Pr(>F)    
## strains       142.535  3 11.265        0.000000003101 ***
## nacl         8002.578  3 33.000 < 0.00000000000000022 ***
## strains:nacl   70.101  6 33.000 < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl |   emmean|        SE|       df|  lower.CL| upper.CL|sig |
## |:--------|:----|--------:|---------:|--------:|---------:|--------:|:---|
## |BR-11001 |0    | 10.84000| 0.5221022| 3.619693|  9.328325| 12.35167|aD  |
## |BR-11001 |150  | 17.74625| 0.5221022| 3.619693| 16.234575| 19.25792|bC  |
## |BR-11001 |300  | 25.33125| 0.5221022| 3.619693| 23.819575| 26.84292|bB  |
## |BR-11001 |450  | 29.64125| 0.5221022| 3.619693| 28.129575| 31.15292|bA  |
## |BR-11002 |0    | 10.75625| 0.5221022| 3.619693|  9.244575| 12.26792|aD  |
## |BR-11002 |150  | 19.40750| 0.5221022| 3.619693| 17.895825| 20.91917|aC  |
## |BR-11002 |300  | 28.38000| 0.5221022| 3.619693| 26.868325| 29.89167|aB  |
## |BR-11002 |450  | 33.04625| 0.5221022| 3.619693| 31.534575| 34.55792|aA  |
## |Control  |0    |  9.36000| 0.5221022| 3.619693|  7.848325| 10.87167|bD  |
## |Control  |150  | 15.19750| 0.5221022| 3.619693| 13.685825| 16.70917|cC  |
## |Control  |300  | 21.74500| 0.5221022| 3.619693| 20.233325| 23.25667|cB  |
## |Control  |450  | 25.58750| 0.5221022| 3.619693| 24.075825| 27.09917|cA  |
## 
## 
## ### Gráfico resumen

## 
## 
## ## Análisis para:  gpx 
## 
## 
## 
## ### Diagnostico de modelo

## > Outliers detectados
## 
## 
## |   |index |strains  |nacl |repetition |  gpx|       resi|   res_MAD| rawp.BHStud|         adjp|    bholm|out_flag |
## |:--|:-----|:--------|:----|:----------|----:|----------:|---------:|-----------:|------------:|--------:|:--------|
## |20 |20    |BR-11002 |0    |R4         | 7.74| -0.7986443| -3.515746|   0.0004385| 0.0004385205| 0.021049|OUTLIER  |
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: gpx
##                     F Df Df.res                Pr(>F)    
## strains        65.977  3 10.985          0.0000002597 ***
## nacl         5923.719  3 32.001 < 0.00000000000000022 ***
## strains:nacl   69.612  6 32.001 < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl |    emmean|        SE|       df|  lower.CL|  upper.CL|sig |
## |:--------|:----|---------:|---------:|--------:|---------:|---------:|:---|
## |BR-11001 |0    |  7.933750| 0.5459773| 3.319524|  6.287096|  9.580404|aD  |
## |BR-11001 |150  | 11.718750| 0.5459773| 3.319524| 10.072096| 13.365404|bC  |
## |BR-11001 |300  | 17.838750| 0.5459773| 3.319524| 16.192096| 19.485404|bB  |
## |BR-11001 |450  | 20.861250| 0.5459773| 3.319524| 19.214596| 22.507904|bA  |
## |BR-11002 |0    |  7.615149| 0.5513238| 3.450493|  5.983326|  9.246973|abD |
## |BR-11002 |150  | 13.461250| 0.5459773| 3.319524| 11.814596| 15.107904|aC  |
## |BR-11002 |300  | 18.953750| 0.5459773| 3.319524| 17.307096| 20.600404|aB  |
## |BR-11002 |450  | 23.381250| 0.5459773| 3.319524| 21.734596| 25.027904|aA  |
## |Control  |0    |  7.148750| 0.5459773| 3.319524|  5.502096|  8.795404|bD  |
## |Control  |150  | 10.266250| 0.5459773| 3.319524|  8.619596| 11.912904|cC  |
## |Control  |300  | 14.838750| 0.5459773| 3.319524| 13.192096| 16.485404|cB  |
## |Control  |450  | 17.778750| 0.5459773| 3.319524| 16.132096| 19.425404|cA  |
## 
## 
## ### Gráfico resumen

## 
## 
## ## Análisis para:  grp 
## 
## 
## 
## ### Diagnostico de modelo

## > Outliers detectados
## 
## 
## |   |index |strains  |nacl |repetition | grp| resi|          res_MAD| rawp.BHStud| adjp| bholm|out_flag |
## |:--|:-----|:--------|:----|:----------|---:|----:|----------------:|-----------:|----:|-----:|:--------|
## |9  |9     |BR-11001 |300  |R1         |  98| -0.5|  -23731533851904|           0|    0|     0|OUTLIER  |
## |10 |10    |BR-11001 |300  |R2         | 100|  1.5|   71194601555710|           0|    0|     0|OUTLIER  |
## |11 |11    |BR-11001 |300  |R3         |  98| -0.5|  -23731533851904|           0|    0|     0|OUTLIER  |
## |12 |12    |BR-11001 |300  |R4         |  98| -0.5|  -23731533851904|           0|    0|     0|OUTLIER  |
## |13 |13    |BR-11001 |450  |R1         |  76| -2.5| -118657669259517|           0|    0|     0|OUTLIER  |
## |14 |14    |BR-11001 |450  |R2         |  78| -0.5|  -23731533851903|           0|    0|     0|OUTLIER  |
## |15 |15    |BR-11001 |450  |R3         |  80|  1.5|   71194601555710|           0|    0|     0|OUTLIER  |
## |16 |16    |BR-11001 |450  |R4         |  80|  1.5|   71194601555710|           0|    0|     0|OUTLIER  |
## |25 |25    |BR-11002 |300  |R1         |  98| -1.0|  -47463067703807|           0|    0|     0|OUTLIER  |
## |26 |26    |BR-11002 |300  |R2         |  98| -1.0|  -47463067703807|           0|    0|     0|OUTLIER  |
## |27 |27    |BR-11002 |300  |R3         | 100|  1.0|   47463067703807|           0|    0|     0|OUTLIER  |
## |28 |28    |BR-11002 |300  |R4         | 100|  1.0|   47463067703807|           0|    0|     0|OUTLIER  |
## |29 |29    |BR-11002 |450  |R1         |  82| -2.0|  -94926135407613|           0|    0|     0|OUTLIER  |
## |31 |31    |BR-11002 |450  |R3         |  86|  2.0|   94926135407614|           0|    0|     0|OUTLIER  |
## |41 |41    |Control  |300  |R1         |  96|  0.5|   23731533851903|           0|    0|     0|OUTLIER  |
## |42 |42    |Control  |300  |R2         |  94| -1.5|  -71194601555711|           0|    0|     0|OUTLIER  |
## |43 |43    |Control  |300  |R3         |  96|  0.5|   23731533851903|           0|    0|     0|OUTLIER  |
## |44 |44    |Control  |300  |R4         |  96|  0.5|   23731533851903|           0|    0|     0|OUTLIER  |
## |45 |45    |Control  |450  |R1         |  56|  3.5|  166120736963325|           0|    0|     0|OUTLIER  |
## |46 |46    |Control  |450  |R2         |  52| -0.5|  -23731533851903|           0|    0|     0|OUTLIER  |
## |47 |47    |Control  |450  |R3         |  52| -0.5|  -23731533851903|           0|    0|     0|OUTLIER  |
## |48 |48    |Control  |450  |R4         |  50| -2.5| -118657669259517|           0|    0|     0|OUTLIER  |
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: grp
##                                                  F Df Df.res
## strains      89732522025707554560428464086064.0000  3 13.111
## nacl           674537293467640588642442006462.0000  2 16.361
## strains:nacl                                2.5243  2 16.003
##                           Pr(>F)    
## strains      <0.0000000000000002 ***
## nacl         <0.0000000000000002 ***
## strains:nacl              0.1115    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl | emmean| SE|       df| lower.CL| upper.CL|sig |
## |:--------|:----|------:|--:|--------:|--------:|--------:|:---|
## |BR-11001 |0    |    100|  0| 15.95945|      100|      100|aA  |
## |BR-11001 |150  |    100|  0| 15.95945|      100|      100|aA  |
## |BR-11001 |450  |       |   |         |         |         |    |
## |BR-11002 |0    |    100|  0| 15.95945|      100|      100|aA  |
## |BR-11002 |150  |    100|  0| 15.95945|      100|      100|aA  |
## |BR-11002 |450  |     84|  0| 18.78373|       84|       84|aB  |
## |Control  |0    |    100|  0| 15.95945|      100|      100|aA  |
## |Control  |150  |    100|  0| 15.95945|      100|      100|aA  |
## |Control  |450  |       |   |         |         |         |    |
## 
## 
## ### Gráfico resumen

## 
## 
## ## Análisis para:  mgt 
## 
## 
## 
## ### Diagnostico de modelo

## > Outliers detectados
## 
## 
## |   |index |strains  |nacl |repetition |      mgt|       resi|   res_MAD| rawp.BHStud|           adjp|     bholm|out_flag |
## |:--|:-----|:--------|:----|:----------|--------:|----------:|---------:|-----------:|--------------:|---------:|:--------|
## |30 |30    |BR-11002 |450  |R2         | 3.166667| -0.4404369| -4.816386|   0.0000015| 0.000001461813| 0.0000702|OUTLIER  |
## |47 |47    |Control  |450  |R3         | 6.115385|  0.3376700|  3.692582|   0.0002220| 0.000221988925| 0.0104335|OUTLIER  |
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: mgt
##                     F Df Df.res                Pr(>F)    
## strains       592.599  3 31.233 < 0.00000000000000022 ***
## nacl         2874.590  3 31.176 < 0.00000000000000022 ***
## strains:nacl   50.111  6 31.279   0.00000000000001103 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl |   emmean|        SE| df| lower.CL| upper.CL|sig |
## |:--------|:----|--------:|---------:|--:|--------:|--------:|:---|
## |BR-11001 |0    | 1.150000| 0.0495327| 34| 1.049338| 1.250663|bD  |
## |BR-11001 |150  | 1.700000| 0.0495327| 34| 1.599338| 1.800663|aC  |
## |BR-11001 |300  | 2.125816| 0.0495327| 34| 2.025154| 2.226479|bB  |
## |BR-11001 |450  | 5.216262| 0.0495327| 34| 5.115599| 5.316924|bA  |
## |BR-11002 |0    | 1.110000| 0.0495327| 34| 1.009337| 1.210662|bD  |
## |BR-11002 |150  | 1.405000| 0.0495327| 34| 1.304337| 1.505662|bC  |
## |BR-11002 |300  | 1.741429| 0.0495327| 34| 1.640766| 1.842091|cB  |
## |BR-11002 |450  | 3.801322| 0.0580728| 34| 3.683304| 3.919340|cA  |
## |Control  |0    | 1.355000| 0.0495327| 34| 1.254338| 1.455663|aD  |
## |Control  |150  | 1.750000| 0.0495327| 34| 1.649337| 1.850663|aC  |
## |Control  |300  | 2.459774| 0.0495327| 34| 2.359111| 2.560436|aB  |
## |Control  |450  | 5.617582| 0.0580728| 34| 5.499564| 5.735601|aA  |
## 
## 
## ### Gráfico resumen

## 
## 
## ## Análisis para:  unc 
## 
## 
## 
## ### Diagnostico de modelo

## > No se detectaron outliers
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: unc
##                     F Df Df.res                Pr(>F)    
## strains       65.2786  3 23.393      0.00000000001624 ***
## nacl         806.2880  3 33.000 < 0.00000000000000022 ***
## strains:nacl   2.9991  6 33.000               0.01887 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl |    emmean|        SE|       df|  lower.CL|  upper.CL|sig |
## |:--------|:----|---------:|---------:|--------:|---------:|---------:|:---|
## |BR-11001 |0    | 0.5316838| 0.0649248| 26.43367| 0.3983355| 0.6650321|bD  |
## |BR-11001 |150  | 1.2286803| 0.0649248| 26.43367| 1.0953321| 1.3620286|aC  |
## |BR-11001 |300  | 1.6943196| 0.0649248| 26.43367| 1.5609713| 1.8276679|aB  |
## |BR-11001 |450  | 2.9065733| 0.0649248| 26.43367| 2.7732250| 3.0399216|aA  |
## |BR-11002 |0    | 0.4961936| 0.0649248| 26.43367| 0.3628453| 0.6295419|bD  |
## |BR-11002 |150  | 0.9185542| 0.0649248| 26.43367| 0.7852059| 1.0519025|bC  |
## |BR-11002 |300  | 1.5535492| 0.0649248| 26.43367| 1.4202010| 1.6868975|aB  |
## |BR-11002 |450  | 2.8281305| 0.0649248| 26.43367| 2.6947822| 2.9614788|aA  |
## |Control  |0    | 0.7738005| 0.0649248| 26.43367| 0.6404522| 0.9071488|aD  |
## |Control  |150  | 1.3523896| 0.0649248| 26.43367| 1.2190414| 1.4857379|aC  |
## |Control  |300  | 1.6980144| 0.0649248| 26.43367| 1.5646661| 1.8313627|aB  |
## |Control  |450  | 2.8561455| 0.0649248| 26.43367| 2.7227973| 2.9894938|aA  |
## 
## 
## ### Gráfico resumen

## 
## 
## ## Análisis para:  syn 
## 
## 
## 
## ### Diagnostico de modelo

## > No se detectaron outliers
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: syn
##                      F Df Df.res                Pr(>F)    
## strains      1743.0005  3 23.735 < 0.00000000000000022 ***
## nacl         1196.5978  3 33.000 < 0.00000000000000022 ***
## strains:nacl    6.1427  6 33.000             0.0002109 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl |    emmean|        SE|       df|  lower.CL|  upper.CL|sig |
## |:--------|:----|---------:|---------:|--------:|---------:|---------:|:---|
## |BR-11001 |0    | 0.8293878| 0.0161694| 27.01915| 0.7962120| 0.8625635|aA  |
## |BR-11001 |150  | 0.6267347| 0.0161694| 27.01915| 0.5935589| 0.6599105|bB  |
## |BR-11001 |300  | 0.3208248| 0.0161694| 27.01915| 0.2876491| 0.3540006|bC  |
## |BR-11001 |450  | 0.1315092| 0.0161694| 27.01915| 0.0983334| 0.1646850|aD  |
## |BR-11002 |0    | 0.8012245| 0.0161694| 27.01915| 0.7680487| 0.8344003|aA  |
## |BR-11002 |150  | 0.7157143| 0.0161694| 27.01915| 0.6825385| 0.7488901|aB  |
## |BR-11002 |300  | 0.4118282| 0.0161694| 27.01915| 0.3786525| 0.4450040|aC  |
## |BR-11002 |450  | 0.1491333| 0.0161694| 27.01915| 0.1159575| 0.1823091|aD  |
## |Control  |0    | 0.7502041| 0.0161694| 27.01915| 0.7170283| 0.7833799|bA  |
## |Control  |150  | 0.5695918| 0.0161694| 27.01915| 0.5364161| 0.6027676|cB  |
## |Control  |300  | 0.3441644| 0.0161694| 27.01915| 0.3109886| 0.3773401|bC  |
## |Control  |450  | 0.1170726| 0.0161694| 27.01915| 0.0838969| 0.1502484|aD  |
## 
## 
## ### Gráfico resumen

## 
## 
## ## Análisis para:  svi 
## 
## 
## 
## ### Diagnostico de modelo

## > No se detectaron outliers
## 
## 
## ### ANOVA tipo III
## 
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: svi
##                     F Df Df.res                Pr(>F)    
## strains      31457.43  3 31.652 < 0.00000000000000022 ***
## nacl          7867.98  3 33.000 < 0.00000000000000022 ***
## strains:nacl    85.97  6 33.000 < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## ### Comparaciones múltiples (Emmeans)
## 
## 
## 
## |strains  |nacl |    emmean|       SE|       df| lower.CL|  upper.CL|sig |
## |:--------|:----|---------:|--------:|--------:|--------:|---------:|:---|
## |BR-11001 |0    |  912.6075| 4.911396| 35.63542| 902.6432|  922.5718|bA  |
## |BR-11001 |150  |  909.6800| 4.911396| 35.63542| 899.7157|  919.6443|bA  |
## |BR-11001 |300  |  694.6879| 4.911396| 35.63542| 684.7235|  704.6522|bB  |
## |BR-11001 |450  |  405.1010| 4.911396| 35.63542| 395.1367|  415.0654|bC  |
## |BR-11002 |0    |  995.5275| 4.911396| 35.63542| 985.5632| 1005.4918|aA  |
## |BR-11002 |150  | 1001.6325| 4.911396| 35.63542| 991.6682| 1011.5968|aA  |
## |BR-11002 |300  |  863.2957| 4.911396| 35.63542| 853.3314|  873.2600|aB  |
## |BR-11002 |450  |  546.1985| 4.911396| 35.63542| 536.2342|  556.1629|aC  |
## |Control  |0    |  782.2000| 4.911396| 35.63542| 772.2357|  792.1643|cA  |
## |Control  |150  |  772.6100| 4.911396| 35.63542| 762.6457|  782.5743|cA  |
## |Control  |300  |  600.6111| 4.911396| 35.63542| 590.6468|  610.5754|cB  |
## |Control  |450  |  159.7628| 4.911396| 35.63542| 149.7984|  169.7271|cC  |
## 
## 
## ### Gráfico resumen

Code

plots <- list(rs$shoot_length$plot +
                scale_y_continuous(limits = c(1, 7)
                                  , breaks = seq(0, 200, by = 1)) +
                labs(x = ""
                     , y = "Shoot length (cm)"
                     ) +
                theme(legend.position = "none"
                      , axis.text.x = element_blank()
                      , strip.text = element_blank()
                      )
              , rs$root_length$plot +
                scale_y_continuous(limits = c(0.5, 4.5)
                                  , breaks = seq(0, 200, by = 0.5)) +
                labs(x = ""
                     , y = "Root length (cm)"
                     ) +
                theme(legend.position = "none"
                      , axis.text.x = element_blank()
                      , strip.text = element_blank()
                      )
              , rs$dry_weight$plot +
                scale_y_continuous(limits = c(0.01, 0.06)
                                  , breaks = seq(0, 100, by = 0.01)) +
                labs(x = "NaCl (mM)"
                     , y = "Dry weight (g)"
                     ) +
                theme(legend.position = "none"
                      , axis.text.y = element_text(angle = 90)
                      )
              , rs$svi$plot +
                scale_y_continuous(limits = c(100, 1100)
                                  , breaks = seq(0, 2000, by = 200)) +
                labs(x = "NaCl (mM)"
                     , y = "Seedling vigor index"
                     ) +
                theme(legend.position = "none"
                      , axis.text.y = element_text(angle = 90)
                      )
              )

# extraer leyenda
legend <- get_plot_component(rs$shoot_length$plot +
                               theme(legend.position = "top"
                                     , legend.direction = "horizontal")
                             , "guide-box", return_all = T)

fgrids <- plots %>% 
  cowplot::plot_grid(plotlist = ., ncol = 2, labels = "auto") 

export <-  list(legend[[4]], fgrids) %>% 
  cowplot::plot_grid(plotlist = .
                     , ncol = 1
                     , rel_heights = c(0.1, 1))
  
plot <- export %>% 
  ggsave(plot = ., "manuscript/Figure-04.jpg", width = 22, height = 20
         , units = "cm")

plot %>% include_graphics()

Code


export %>% 
  ggsave(plot = ., "manuscript/Figure-04.eps", width = 22, height = 20
         , units = "cm")

4.4 To evaluate physiological responses to salinity through the activity of antioxidant enzymes (SOD, CAT, APX, GPX)

Code

plots <- list(rs$sod$plot +
                scale_y_continuous(limits = c(20, 70)
                                  , breaks = seq(0, 200, by = 10)) +
                labs(x = ""
                     , y = "Superoxide dismutase (SOD)"
                     ) +
                theme(legend.position = "none"
                      , axis.text.x = element_blank()
                      , strip.text = element_blank()
                      )
              , rs$cat$plot +
                scale_y_continuous(limits = c(10, 50)
                                  , breaks = seq(0, 200, by = 10)) +
                labs(x = ""
                     , y = "Catalase (CAT)"
                     ) +
                theme(legend.position = "none"
                      , axis.text.x = element_blank()
                      , strip.text = element_blank()
                      )
              , rs$apx$plot +
                scale_y_continuous(limits = c(7.5, 35)
                                  , breaks = seq(0, 100, by = 5)) +
                labs(x = "NaCl (mM)"
                     , y = "Ascorbate peroxidase (APX)"
                     ) +
                theme(legend.position = "none"
                      )
              , rs$gpx$plot +
                scale_y_continuous(limits = c(5, 25)
                                  , breaks = seq(0, 100, by = 5)) +
                labs(x = "NaCl (mM)"
                     , y = "Guaiacol peroxidase (GPX)"
                     ) +
                theme(legend.position = "none"
                      )
              )

# extraer leyenda
legend <- get_plot_component(rs$shoot_length$plot +
                               theme(legend.position = "top"
                                     , legend.direction = "horizontal")
                             , "guide-box", return_all = T)

fgrids <- plots %>% 
  cowplot::plot_grid(plotlist = ., ncol = 2, labels = "auto") 

export <-  list(legend[[4]], fgrids) %>% 
  cowplot::plot_grid(plotlist = .
                     , ncol = 1
                     , rel_heights = c(0.1, 1))
  
plot <- export %>% 
  ggsave(plot = ., "manuscript/Figure-05.jpg", width = 22, height = 20
         , units = "cm")

plot %>% include_graphics()

Code


export %>% 
  ggsave(plot = ., "manuscript/Figure-05.eps", width = 22, height = 20
         , units = "cm")

5 Interaction phenotipic and biochemical

Code

fbclean <- 1:length(rs) %>%  map( \(x) { 
  
  rs[[x]]$mc %>% 
    dplyr::select(1:emmean) %>% 
    dplyr::rename(!!names(rs)[x] := emmean)
  
  }) %>% 
  Reduce(function(...) merge(..., all = TRUE), .)

Code

dta <- fbclean %>% 
  dplyr::select(!c(fresh_weight
                   , ms_porc, humidity_cont, humidity_porc, 
                   , grp:syn
                   )) %>% 
  unite("treat", c(strains, nacl), remove = F, sep = "_") %>% 
  column_to_rownames("treat")

mv <- PCA(X = dta, scale.unit = T, quali.sup = 1:2, graph = F)

var <- plot.PCA(mv, choix = c("var")
                , autoLab = "yes"
                , cex = 1.2
                ) 

varcor <- mv$ind$coord %>% 
  data.frame() %>% 
  rownames_to_column("treat") %>% 
  separate(treat, c("G1", "G2"), remove = F, sep = "_")

test <- plot.PCA(mv
                , choix = c("ind")
                , autoLab = "yes"
                , cex = 0.8
                , invisible = "quali"
                , habillage = 1
                )

ind <- ggplot(varcor,
                aes(x = Dim.1,
                    y = Dim.2,
                    color = G1,
                    shape = G1,
                    label = treat)) +
  geom_point(size = 1.8) +
  ggrepel::geom_text_repel(size = 4, max.overlaps = Inf
                           ) +  
  scale_shape_manual(values = c(23, 22, 17, 24, 18, 21)) +  
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
  geom_vline(xintercept = 0, linetype = "dashed", color = "black") +
  theme_classic(base_size = 9) +
  theme(
    legend.position = "bottom",
    legend.box = "horizontal",
    panel.border = element_rect(colour = "black", fill = NA, linewidth = 0.6),
    plot.title = element_text(hjust = 0.5, face = "bold") 
  ) +
  labs(
    title = "PCA graph of individuals",
    x = paste0("Dim 1 (", round(mv$eig[1, 2], 2), "%)"),
    y = paste0("Dim 2 (", round(mv$eig[2, 2], 2), "%)"),
    color = "Treatment",
    shape = "Treatment"
  ) +
  guides(
    color = guide_legend(nrow = 1, byrow = TRUE),
    shape = guide_legend(nrow = 1, byrow = TRUE)
  )

grid <- list(var, ind) %>% 
  plot_grid(plotlist = ., ncol = 2, labels = "AUTO", rel_widths = c(1, 1.3))

summary(mv, nbelements = Inf, nb.dec = 1)
## 
## Call:
## PCA(X = dta, scale.unit = T, quali.sup = 1:2, graph = F) 
## 
## 
## Eigenvalues
##                      Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7 Dim.8
## Variance               5.9   1.7   0.3   0.0   0.0   0.0   0.0   0.0
## % of var.             74.3  21.6   3.5   0.4   0.1   0.0   0.0   0.0
## Cumulative % of var.  74.3  96.0  99.5  99.9 100.0 100.0 100.0 100.0
## 
## Individuals
##                Dist   Dim.1  ctr cos2   Dim.2  ctr cos2   Dim.3  ctr cos2  
## BR-11001_0   |  2.9 |  -2.8 10.7  0.9 |  -0.9  3.5  0.1 |   0.2  1.7  0.0 |
## BR-11001_150 |  1.6 |  -1.5  3.0  0.9 |   0.3  0.4  0.0 |   0.5  6.5  0.1 |
## BR-11001_300 |  1.4 |   1.2  2.2  0.8 |   0.4  0.8  0.1 |   0.3  3.6  0.1 |
## BR-11001_450 |  3.3 |   3.2 14.8  1.0 |   0.2  0.1  0.0 |  -0.3  3.5  0.0 |
## BR-11002_0   |  3.8 |  -3.7 19.1  0.9 |   0.5  1.0  0.0 |  -1.0 26.7  0.1 |
## BR-11002_150 |  2.9 |  -2.1  6.3  0.5 |   1.9 18.0  0.4 |  -0.6 11.8  0.0 |
## BR-11002_300 |  2.2 |   0.8  0.8  0.1 |   1.8 16.3  0.7 |   0.8 19.4  0.1 |
## BR-11002_450 |  3.4 |   3.0 12.8  0.8 |   1.6 13.1  0.2 |  -0.2  0.8  0.0 |
## Control_0    |  2.9 |  -2.2  6.7  0.6 |  -1.9 17.9  0.4 |   0.2  0.7  0.0 |
## Control_150  |  1.5 |  -1.0  1.5  0.5 |  -1.0  4.9  0.4 |   0.3  3.3  0.0 |
## Control_300  |  1.6 |   1.2  2.0  0.6 |  -0.9  4.1  0.3 |   0.5  6.2  0.1 |
## Control_450  |  4.4 |   3.8 20.3  0.8 |  -2.0 19.9  0.2 |  -0.7 15.8  0.0 |
## 
## Variables
##                Dim.1  ctr cos2   Dim.2  ctr cos2   Dim.3  ctr cos2  
## shoot_length |  -0.9 13.1  0.8 |   0.4  9.8  0.2 |   0.2 12.9  0.0 |
## root_length  |  -0.8 11.9  0.7 |   0.5 15.4  0.3 |   0.1  2.2  0.0 |
## dry_weight   |  -0.7  7.3  0.4 |   0.6 21.7  0.4 |  -0.4 67.5  0.2 |
## sod          |   0.9 14.0  0.8 |   0.4  9.5  0.2 |   0.0  0.3  0.0 |
## cat          |   0.9 13.5  0.8 |   0.4 11.2  0.2 |   0.0  0.9  0.0 |
## apx          |   0.9 13.3  0.8 |   0.5 11.9  0.2 |   0.1  0.9  0.0 |
## gpx          |   0.9 13.5  0.8 |   0.4 11.1  0.2 |   0.0  0.1  0.0 |
## svi          |  -0.9 13.3  0.8 |   0.4  9.4  0.2 |   0.2 15.2  0.0 |
## 
## Supplementary categories
##                Dist   Dim.1 cos2 v.test   Dim.2 cos2 v.test   Dim.3 cos2 v.test
## Control      |  1.5 |   0.4  0.1    0.4 |  -1.5  0.9   -2.6 |   0.1  0.0    0.2
## BR-11001     |  0.2 |   0.1  0.1    0.1 |   0.0  0.0    0.0 |   0.2  0.7    0.8
## BR-11002     |  1.6 |  -0.5  0.1   -0.5 |   1.5  0.9    2.6 |  -0.2  0.0   -1.0
## 0            |  3.0 |  -2.9  0.9   -2.3 |  -0.8  0.1   -1.1 |  -0.2  0.0   -0.7
## 150          |  1.6 |  -1.5  0.9   -1.2 |   0.4  0.1    0.6 |   0.1  0.0    0.2
## 300          |  1.3 |   1.1  0.7    0.8 |   0.4  0.1    0.6 |   0.5  0.2    1.9
## 450          |  3.4 |   3.4  1.0    2.6 |  -0.1  0.0   -0.1 |  -0.4  0.0   -1.5
##               
## Control      |
## BR-11001     |
## BR-11002     |
## 0            |
## 150          |
## 300          |
## 450          |
pcainfo <- factoextra::get_pca_var(mv)
pcainfo$cor
##                   Dim.1     Dim.2       Dim.3         Dim.4        Dim.5
## shoot_length -0.8834916 0.4128814  0.19120332  0.1084074234 -0.009196021
## root_length  -0.8409049 0.5163196  0.07891082 -0.1408456601 -0.002664913
## dry_weight   -0.6571464 0.6135355 -0.43684452  0.0296108767  0.002858408
## sod           0.9131964 0.4051834  0.02772554  0.0057793927  0.025941027
## cat           0.8959367 0.4402613  0.04999076  0.0003493301  0.021261073
## apx           0.8892093 0.4541223  0.05164879  0.0073325239  0.007160950
## gpx           0.8972543 0.4376526  0.01862562 -0.0035068445 -0.054244469
## svi          -0.8905875 0.4029004  0.20720447  0.0136610840  0.010017527
pcainfo$contrib
##                  Dim.1     Dim.2      Dim.3         Dim.4      Dim.5
## shoot_length 13.129524  9.844814 12.9258200 35.8815440952  1.9580628
## root_length  11.894273 15.395514  2.2016099 60.5675516813  0.1644345
## dry_weight    7.263877 21.738837 67.4716012  2.6770446200  0.1891800
## sod          14.027249  9.481131  0.2717861  0.1019805819 15.5812042
## cat          13.502022 11.193809  0.8835817  0.0003725847 10.4663935
## apx          13.300016 11.909748  0.9431648  0.1641572445  1.1873207
## gpx          13.541763 11.061552  0.1226560  0.0375479392 68.1298774
## svi          13.341276  9.374594 15.1797803  0.5698012532  2.3235270

plot <- grid %>% 
  ggsave2(plot = ., "manuscript/Figure-06.eps"
          , width = 35
          , height = 15
          , units = "cm")

plot <- grid %>% 
  ggsave2(plot = ., "manuscript/Figure-06.jpg"
          , width = 35
          , height = 15
          , units = "cm")

include_graphics(plot)